class_docstr: A trainable interface for a simple, generic encoder + decoder model
  
  # Arguments
  encoder: an encoder object
  decoder: a decoder object
  input_shape: shape of encoder input, for variable length, please use -1 as seq len
  output_shape: shape of decoder input, for variable length, please use -1 as seq len
  bridge: connect encoder and decoder
  generator: Feeding decoder output to generator to generate final result, `None` is supported
  
  >>> encoder = RNNEncoder.initialize("LSTM", 1, 4)
  creating: createZooKerasLSTM
  creating: createZooKerasRNNEncoder
  >>> decoder = RNNDecoder.initialize("LSTM", 1, 4)
  creating: createZooKerasLSTM
  creating: createZooKerasRNNDecoder
  >>> bridge = Bridge.initialize("dense", 4)
  creating: createZooKerasBridge
  >>> seq2seq = Seq2seq(encoder, decoder, [2, 4], [2, 4], bridge)
  creating: createZooKerasInput
  creating: createZooKerasInput
  creating: createZooKerasSelectTable
  creating: createZooKerasModel
  creating: createZooSeq2seq
docstr: Load an existing Seq2seq model (with weights).
  
  # Arguments
  path: The path for the pre-defined model.
        Local file system, HDFS and Amazon S3 are supported.
        HDFS path should be like 'hdfs://[host]:[port]/xxx'.
        Amazon S3 path should be like 's3a://bucket/xxx'.
  weight_path: The path for pre-trained weights if any. Default is None.
docstr_tok: ["Load","an","existing","Seq2seq","model","(","with","weights",")",".","#","Arguments","path",":","The","path","for","the","pre-defined","model",".","Local","file","system",",","HDFS","and","Amazon","S3","are","supported",".","HDFS","path","should","be","like","'hdfs",":","\/\/","[","host","]",":","[","port","]","\/xxx","'",".","Amazon","S3","path","should","be","like","'s3a",":","\/\/bucket\/xxx","'",".","weight_path",":","The","path","for","pre-trained","weights","if","any",".","Default","is","None","."]
code: def load_model(path, weight_path=None, bigdl_type="float"):
      """
      Load an existing Seq2seq model (with weights).
  
      # Arguments
      path: The path for the pre-defined model.
            Local file system, HDFS and Amazon S3 are supported.
            HDFS path should be like 'hdfs://[host]:[port]/xxx'.
            Amazon S3 path should be like 's3a://bucket/xxx'.
      weight_path: The path for pre-trained weights if any. Default is None.
      """
      jmodel = callZooFunc(bigdl_type, "loadSeq2seq", path, weight_path)
      model = ZooModel._do_load(jmodel, bigdl_type)
      model.__class__ = Seq2seq
      return model
  
imports: from bigdl.nn.layer import Layer
  from zoo.pipeline.api.keras.layers import *
  from zoo.models.common import ZooModel
  from zoo.common.utils import callZooFunc
  from zoo.pipeline.api.keras.engine import ZooKerasLayer
  from zoo.pipeline.api.keras.models import Model
label_desc: Use image node_id 4 for calling the Seq2seq obj's underlying member method code with example usage: obj.load_model(path, weight_path, bigdl_type) and returns: model